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Updated: Mar 20, 2026
Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma
Published on: September 13, 2022
Bioinformatic profiling identifies an immune-related risk signature for glioblastoma
Wen Cheng1, Xiufang Ren1, Chuanbao Zhang1
1From the Department of Neurosurgery (W.C., Y.L., S.H., A.W.), The First Hospital of China Medical University; Department of Pathology (X.R.), Shengjing Hospital of China Medical University, Shenyang; Beijing Neurosurgical Institute (C.Z.), Capital Medical University; and Department of Neurosurgery (J.C.), The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Objective:
To investigate the local immune status and its prognostic value in glioma.
Methods:
A cohort of 297 glioma samples with whole genome microarray expression data from the Chinese Glioma Genome Atlas database were included for discovery. The Cancer Genome Atlas (TCGA) database was used for validation. Principal components analysis and gene set enrichment analysis were used to explore the bioinformatic implication.
Results:
Distinct local immune status was identified according to histologic grade. Glioblastoma (GBM) exhibited an enhanced immune phenotype compared to lower grade glioma. We profiled the immune-related gene set and identified 8 genes (FOXO3, IL6, IL10, ZBTB16, CCL18, AIMP1, FCGR2B, and MMP9) with the greatest prognostic value in GBM. A local immune-related risk signature was developed from the genes to distinguish cases as high or low risk of unfavorable prognosis, which could be validated in TCGA database. High-risk patients conferred an enhanced intensity of local immune response compared to low-risk ones. Additionally, the signature exhibited different distribution based on molecular features. The signature had prognostic significance in the stratified cohorts and was identified as an independent prognostic factor for GBM.
Conclusions:
We profiled the immune status in glioma and established a local immune signature for GBM, which could independently identify patients with a high risk of reduced survival, indicating the relationship between prognosis and local immune response.
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